/**
 * Gold-set tagging API (#81.7 / #81.8).
 *
 * The chair/Dafna manually labels a stratified sample of halachot
 * (is_holding / correct_type / quote_complete). Those human labels are the
 * ground truth used to measure the extraction validators and recalibrate the
 * auto-approve threshold. Endpoints under /api/goldset.
 */
import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query";
import { apiRequest } from "./client";

export type GoldsetItem = {
  id: string;
  halacha_id: string;
  // human tags (null until tagged)
  is_holding: boolean | null;
  correct_type: string;
  quote_complete: boolean | null;
  tagged_by: string;
  tagged_at: string | null;
  // halacha content + the machine's own labels
  rule_statement: string;
  supporting_quote: string;
  reasoning_summary: string;
  rule_type: string;
  confidence: number | null;
  quality_flags?: string[];
  review_status: string;
  case_number: string | null;
  case_name: string | null;
  source_type: string | null;  // 'court_ruling' | 'appeals_committee' | ''
  // AI second-opinion (QA aid — independent, not ground truth, not auto-applied)
  ai_is_holding: boolean | null;
  ai_correct_type: string;
  ai_rationale: string;
  ai_generated_at: string | null;
};

export type GoldsetScore = {
  batch: string;
  total: number;
  labeled: number;
  validators: Record<
    string,
    { precision: number; recall: number; f1: number; tp: number; fp: number; fn: number; tn: number }
  >;
};

export type GoldsetTag = {
  is_holding?: boolean | null;
  correct_type?: string;
  quote_complete?: boolean | null;
};

const keys = {
  all: ["goldset"] as const,
  list: (batch: string) => ["goldset", "list", batch] as const,
  score: (batch: string) => ["goldset", "score", batch] as const,
};

export function useGoldset(batch = "default") {
  return useQuery({
    queryKey: keys.list(batch),
    queryFn: ({ signal }) =>
      apiRequest<{ items: GoldsetItem[]; batch: string }>(
        `/api/goldset?batch=${encodeURIComponent(batch)}`,
        { signal },
      ),
    staleTime: 5_000,
    refetchOnMount: "always",
  });
}

export function useGoldsetScore(batch = "default") {
  return useQuery({
    queryKey: keys.score(batch),
    queryFn: ({ signal }) =>
      apiRequest<GoldsetScore>(
        `/api/goldset/score?batch=${encodeURIComponent(batch)}`,
        { signal },
      ),
    staleTime: 5_000,
  });
}

export function useTagGoldset(batch = "default") {
  const qc = useQueryClient();
  return useMutation({
    mutationFn: ({ id, tag }: { id: string; tag: GoldsetTag }) =>
      apiRequest<{ ok: boolean }>(`/api/goldset/${encodeURIComponent(id)}`, {
        method: "PATCH",
        body: { ...tag, tagged_by: "chair" },
      }),
    onSuccess: () => {
      qc.invalidateQueries({ queryKey: keys.list(batch) });
      qc.invalidateQueries({ queryKey: keys.score(batch) });
    },
  });
}

export function useCreateGoldsetSample(batch = "default") {
  const qc = useQueryClient();
  return useMutation({
    mutationFn: (n: number) =>
      apiRequest<{ batch: string; inserted: number; total: number }>(
        "/api/goldset/sample",
        { method: "POST", body: { n, batch } },
      ),
    onSuccess: () => qc.invalidateQueries({ queryKey: keys.list(batch) }),
  });
}